Abstract Details
Activity Number:
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290
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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Abstract #312098
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View Presentation
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Title:
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Single-Index Modulated Multiple Testing
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Author(s):
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Lilun Du*+ and Chunming Zhang
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Companies:
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University of Wisconsin-Madison and University of Wisconsin-Madison
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Keywords:
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Bivariate normality ;
Local false discovery rate ;
Multiple comparison ;
p-value ;
Simultaneous inference ;
Symmetry property
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Abstract:
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In the context of large-scale multiple testing, hypotheses are often accompanied with certain prior information. In this talk, we present a single-index modulated (SIM) multiple testing procedure, which maintains control of the false discovery rate while incorporating prior information, by assuming the availability of a bivariate p-value, (p1, p2), for each hypothesis, where p1 is a preliminary p-value from prior information and p2 is the primary p-value for the ultimate analysis. To find the optimal rejection region for the bivariate p-value, we propose a criteria based on the ratio of probability density functions of (p1, p2) under the true null and non-null. This criteria in the bivariate normal setting further motivates us to project the bivariate p-value to a single-index, p(t), for a wide range of directions theta. The true null distribution of p(t) is estimated via parametric and nonparametric approaches, leading to two procedures for estimating and controlling the false discovery rate. To derive the optimal projection direction theta, we propose a new approach based on power comparison, which is further shown to be consistent under some mild conditions.
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Authors who are presenting talks have a * after their name.
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